Abstract:

Compilation to boolean satisfiability has become a powerful paradigm
for solving AI problems. However, domains that require metric
reasoning cannot be compiled efficiently to SAT even if they would
otherwise benefit from compilation. We address this problem by
introducing the LCNF representation which combines propositional
logic with metric constraints. We present LPSAT, an engine which
solves LCNF problems by interleaving calls to an incremental
simplex algorithm with systematic satisfaction methods. We describe
a compiler which converts metric resource planning
problems into LCNF for processing by LPSAT. The experimental
section of the paper explores several optimizations to LPSAT,
including learning from constraint failure and randomized cutoffs.